Abstract--Variation at 14 microsatellite loci was examined in 34
chum salmon (Oncorhynchus keta) populations from Russia and evaluated
for its use in the determination of population structure and stock
composition in simulated mixed-stock fishery samples. The genetic
differentiation index ([F.sub.st]) over all populations and loci was
0.017, and individual locus values ranged from 0.003 to 0.054. Regional
population structure was observed, and populations from Primorye,
Sakhalin Island, and northeast Russia were the most distinct.
Microsatellite variation provided evidence of a more fine-scale
population structure than those that had previously been demonstrated
with other genetic-based markers. Analysis of simulated mixed-stock
samples indicated that accurate and precise regional estimates of stock
composition were produced when the microsatellites were used to estimate
stock compositions. Microsatellites can be used to determine stock
composition in geographically separate Russian coastal chum salmon
fisheries and provide a greater resolution of stock composition and
population structure than that previously provided with other
techniques.

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In Asia, there are two distinct types of chum salmon (Oncorhynchus
keta Walbaum). The early-maturing or "summer" chum salmon
generally returns to spawn from June through August in streams bordering
Kamchatka, the Sea of Okhotsk, the east coast of Sakhalin Island, and
the Amur River. Later-maturing or "autumn" chum salmon
generally return to spawn from September through November in streams in
Japan, the southern Kuril Islands, the west coast of Sahkalin Island,
and the Amur River (Sano, 1966). In general, summer chum salmon spawn in
areas where egg incubation occurs in subsurface stream flow, whereas
autumn chum salmon spawn in areas of groundwater upwelling (Volobuyev et
al., 1990). In major river drainages, autumn chum salmon generally
migrate further up the drainage to spawn than do summer chum salmon, and
are larger, younger, and more fecund than the summer-run fish (Sano,
1966).

Determination of the origin of salmon in mixed-stock fisheries is
important for effective management. For chum salmon in Asia, scale
pattern variation has provided a technique for the determination of
origin of individuals to large geographic areas (Tanaka et al., 1969;
Ishida et al., 1989), and in some cases reportedly to a specific river
drainage (Nikolayeva and Semenets, 1983). Trace elements in otoliths
have also been reported to be effective for stock identification of
Korean populations (Sohn et al., 2005). Stock identification techniques
based on scale pattern analysis have generally been replaced by
applications based on genetic variation, owing to the increased
resolution that is possible by applying genetic variation (see example
outlined by Wilmot et al. [1998]). Analyses of genetic variation have
been demonstrated to be effective in determining salmonid population
structure, as well as determining origins of salmon in mixed-stock
fisheries. For Russian chum salmon, analyses of allozyme variation have
indicated differentiation among populations on the east and west coasts
of Kamchatka (Winans et al., 1994), and either marignal (Salmenkova et
al., 2007) or some level of differentiation between populations on
Sakhalin Island and populations on the mainland Russian coast (Efremov,
2001). Populations in the far northeastern portions of mainland Russia
were distinct from populations in western Alaska (Wilmot et al., 1994).
Surveys of allozyme variation have generally indicated regional
population differentiation among Russian populations.

DNA-level markers have substantially increased the number of
polymorphic loci that are available to be included in analyses of
genetic variation. Initial surveys of mitochondrial (mt) DNA variation
indicated regional differentiation between Sakhalin Island and mainland
populations (Ginatulina, 1992). Later analyses of additional mtDNA
variation indicated marked differentiation between Japanese and Russian
populations (Sato et al., 2004), and some differentiation among Russian
populations (Brykov et al., 2003; Polyakova et al., 2006). Limited
examinations of minisatellite variation have indicated some level of
differentiation between Japanese and Russian populations, but have
yielded little evidence of regional structure for Russian populations
(Taylor et al., 1994; Beacham, 1996).

Analyses of microsatellite variation have been effective for
determining salmonid population structure in local areas (Small et al.,
1998; Banks et al., 2000; Beacham et al., 2004), as well as broad-scale
differences across the Pacific Rim (Beacham et al., 2005, 2006).
Microsatellites have also been of considerable value in estimating stock
composition in mixed-stock salmon fisheries, on both a
population-specific (Beacham et al., 2003) and regional basis (Beacham
et al., 2006). Micro-satellite variation in chum salmon provides the
means to examine fine-scale population structure (Chen et al., 2005), as
well as the means for fine-scale estimation of stock composition in
mixed-stock fisheries (Beacham et al., in press). Analyses of
microsatellite variation in Russian chum populations would likely be of
value by providing increased resolution of population structure compared
with that provided by previous techniques, and would likely aid in
increasing accuracy and precision of estimates of stock composition in
mixed-stock fishery samples.

[FIGURE 1 OMITTED]

Our objectives were to analyze the variation at 14 microsatellite
loci to evaluate population structure of Russian chum salmon populations
from the far north eastern coast of Russia to the more southern areas of
Primorye and Sakhalin Island, and then to evaluate the use of these loci
for the practical purpose of providing accurate and precise estimates of
stock composition in mixed-stock fishery samples. Stock composition
evaluation was accomplished by the analysis of simulated mixed-stock
fishery samples.

In general, PCR DNA amplifications were conducted by using DNA
Engine Cycler Tetrad2 (BioRad, Hercules, CA) in 6-[micro]L volumes
consisting of 0.15 units of Taq polymerase, 1 [micro]L (25-50 ng) of
extracted DNA, 1 x PCR buffer (Qiagen, Mississauga, Ontario, Canada), 60
[micro]M each nucleotide, 0.40 [micro]M of each primer, and deionized
water. The thermal cycling profile involved one cycle of 15 minutes at
95[degrees]C, followed by 30-40 cycles of 20 seconds at 94[degrees]C,
30-60 seconds at 47-65[degrees]C, and 30-60 seconds at 68-72[degrees]C
(depending on the locus). Specific PCR conditions for a particular locus
could vary from this general outline. PCR fragments were initially size
fractionated in denaturing polyacrylamide gels by using an ABI 377
automated DNA sequencer, and genotypes were scored by Genotyper 2.5
software (Applied Biosystems, Foster City, CA) using an internal lane
sizing standard. Later in the study, microsatellites were size
fractionated in an ABI 3730 capillary DNA sequencer, and genotypes were
scored by GeneMapper software 3.0 (Applied Biosystems, Foster City, CA)
by using an internal lane-sizing standard. Allele identification between
the two sequencers was standardized by analyzing the same approximately
600 individuals on both platforms and converting the sizing in the
gel-based data set to match that obtained from the capillary-based set.

Data analysis

Each population at each locus was tested for departure from
Hardy-Weinberg equilibrium (HWE) by using genetic data analysis (GDA).
Critical significance levels for simultaneous tests (34 populations,
Table 1) were evaluated using Bonferroni adjustment (0.05/34=0.0015)
(Rice, 1989). All annual samples available for a location were combined
to estimate population allele frequencies, as was recommended by Waples
(1990). [F.sub.st] estimates for each locus were calculated with FSTAT
(Goudet, 1995), individual locus values were determined by jackknifing over populations, and the overall [F.sub.st] estimate was determined by
jackknifing over loci (Goudet, 1995). Inter-regional comparisons of
[F.sub.st] estimates were determined by calculation of all appropriate
pairwise point estimates of [F.sub.st] values, and then determining the
mean and standard deviation of these values. The Cavalli-Sforza and
Edwards (CSE) (1967) chord distance was used to estimate distances among
populations. An unrooted neighbor-joining tree based upon CSE was
generated using NJPLOT (Perriere and Gouy, 1996). Bootstrap support (by
sampling loci) for the major nodes in the dendrogram was evaluated with
the CONSENSE program from PHYLIP (Univ. Washington, Seattle, WA) and
based on 500 replicate trees. Computation of the number of alleles
observed per locus, as well as allelic diversity standardized to a
common sample size, was carried out with FSTAT.

Estimation of stock composition

Genotypic frequencies were determined for each locus in each
population, and the Statistical Package for the Analysis of Mixtures
software program (SPAM, vers. 3.7, Debevec et al., 2000) was used to
estimate stock composition of simulated mixtures. The Rannala and
Mountain (1997) correction to baseline allele frequencies was used in
the analysis in order to accommodate the occurrence of fish in the mixed
sample that were from a specific population having an allele not
observed in the baseline samples from that population. All loci were
considered to be in Hardy-Weinberg equilibrium, and expected genotypic
frequencies were determined from the observed allele frequencies.
Reported stock compositions for simulated fishery samples were the
bootstrap mean estimate of each mixture of 150 fish analyzed, and mean
and variance estimates were derived from 100 bootstrap simulations. Both
the baseline population and the simulated single-population were sampled
with replacement in order to simulate random variation involved in the
collection of the baseline and fishery samples.

Results

Variation within and among populations

The observed number of alleles observed at a locus ranged from 21
alleles at Oke3 and Oki2 to 138 alleles at One111 (Table 2). Lower
expected heterozygosities were generally observed at loci with fewer
alleles. The genotypic frequencies observed at the 14 loci generally
conformed to those expected under Hardy-Weinberg equilibrium (HWE) after
Bonferroni correction. For the Oke3 and OtsG68 loci, a minor HWE
nonconformance of genotypic frequencies was observed, and observed
heterozygosities were 2-6% less than those expected (Table 2).

Genetic diversity, with respect to the number of alleles observed,
was evident among regional groups of chum salmon. Chum salmon
populations from Primorye, the northern Sea of Okhotsk, and northeast
Russia displayed fewer alleles (mean 320 alleles) than did populations
in Magadan, west Kamchatka, and east Kamchatka (mean 370 alleles) (Table
3). Chum salmon from the latter regions displayed approximately 16% more
alleles than did those from the former regions. The greatest
differentiation in allelic diversity was observed at those loci with
greater numbers of alleles, particularly at locus One111.

Population structure

Genetic differentiation was evident among chum salmon populations
from the different geographic regions surveyed. The [F.sub.st] value
over all 34 populations and 14 loci surveyed was 0.017, and individual
locus values ranged from 0.003 (One102) to 0.054 (Ots3) (Table 2). Chum
salmon populations from Primorye and the Amur River were well defined
compared with other regional populations (Table 4). Populations from the
southwestern portion of Russia (Primorye, Amur River, Sakhalin Island)
were most distinct from those in more northern and eastern regions
(Magadan, Sea of Okhotsk, Kamchatka, northeast Russia).

Regional clustering of population samples was observed in the
analysis of population structure. Strong clustering of population
samples from the Primorye region was observed; the three population
samples included in the analysis clustered together in 100% of the trees
examined (Fig. 2). Similarly, strong clustering was observed in most of
the population samples from Sakhalin Island, as well as the two
population samples from the northern coast of the Sea of Okhotsk. The
two population samples from northeast Russia clustered together in 100%
of the trees examined, together with the Utka River population sample
from west Kamchatka. Although there was a general clustering of
population samples from east and west Kamchatka, these regional
groupings were not strongly supported in the cluster analysis.

Stock identification

Genetic differentiation observed among the chum salmon in the
regions surveyed was evaluated to determine if it was sufficient for a
mixed-stock analysis with the objective of obtaining accurate regional
stock compositions. Analysis of simulated single-region samples
indicated that estimates of the contribution of chum salmon from that
region were usually greater than 89% (Table 5), although there was the
expectation that errors in estimation for the region in question would
be maximized when the single region comprised 100% of the simulated
sample. The level of accuracy observed in the estimated stock
compositions indicated that accurate regional estimates of stock
composition should be obtained in samples containing individuals from
multiple regions.

Testing of accuracy of regional estimates of stock composition in
multiregion mixtures was conducted by evaluating four simulated fishery
samples. Estimated stock composition of a simulated mixture containing
chum salmon from Primorye, Sakhalin Island, the Amur River, and Magadan
was within 3% of the actual population composition and 4% of the
regional composition (Table 6, mixture 1). Similar results were observed
for a simulated mixture comprising chum salmon from Sakhalin Island,
Magadan, the northern Sea of Okhotsk, and west Kamchatka, with
population estimates within 3% of actual composition and regional
estimates within 3% (Table 6, mixture 2). Estimated stock compositions
of a simulated mixture of chum salmon from west Kamchatka, east
Kamchatka, and northeast Russia were within 6% of actual population
composition and within 5% for regional contributions (Table 6, mixture
3). Regional estimated stock compositions of a more complex simulated
sample of fish from multiple regions were within 3% of actual regional
contributions (Table 6, mixture 4). Accurate regional estimates of stock
composition should be obtained when the current baseline is applied to
mixed-stock samples of chum salmon taken from Russian coastal waters,
provided that the individuals in the mixture originate entirely from
Russian populations.

[FIGURE 2 OMITTED]

Discussion

Population structure

The range of populations sampled in the study required a concerted
sampling effort, and in some locations collection of appropriate samples
proved to be difficult. The number of fish sampled from a site or
population ranged from 17 to 338 individuals. Estimated population-level
allele frequencies will be subject to relatively larger sampling error
at smaller population sample sizes, particularly for loci with large
numbers of alleles such as One111. Small sample size may have
contributed to errors on allele-frequency estimates for some
populations. Sampling error may obscure genetic relationships among
related populations, or conversely genetic relationships among some
populations may be falsely inferred. However, the available evidence
indicates that variation in population sample sizes did not obscure
relationships among related populations. For example, we analyzed chum
salmon from three sampling sites in Primorye with sample sizes ranging
between 17 and 49 fish per site,. Although these sample sizes were
small, and thus the estimation of allele frequencies would be subject to
sampling error, the clustering of these populations was well supported
by our bootstrap calculations (100%). Regional clustering of samples or
populations is typically observed in chum salmon (Beacham et al., 1987;
Winans et al., 1994), and thus it is unlikely that close genetic
relationships among these populations from Primorye were inferred
incorrectly.

If populations spawn in remote areas, opportunities to collect
samples may be limited. In our study, all samples that were available
for a specific sampling site or population were combined in order to
estimate genetic differentiation among populations. Annual variation in
allele frequencies within a population is typically less than the
geographic and population differences observed; therefore pooling annual
samples over time is a reasonable approach to estimate population-level
allele frequencies. Relative annual stability of microsatellite allele
frequencies is a general feature of microsatellite loci in salmonids
(Tessier and Bernatchez, 1999; Beacham et al., 2006).

The population structure of chum salmon in Russia has been
investigated previously. For example, Winans et al. (1994) after
examining 35 allozyme loci, indicated that there were four groups of
Russian chum salmon populations, and that one group generally comprised
populations from west Kamchatka, a second group comprised populations
from Magadan, the Sea of Okhotsk, and east Kamchatka, the third group
was solely from east Kamchatka, and a fourth group comprised the
populations from the Utka River in west Kamchatka and the Anadyr River in northeast Russia. In our study, some similarities were observed
between Magadan and the Sea of Okhotsk regional population groups, as
well as between the groups from east and west Kamchatka. Winans et al.
(1994) did not include populations from Primorye or Sakhalin Island in
their survey, but Ginatulina (1992) had previously demonstrated clear
differentiation of mitochondrial genotypes between the populations from
these two regions. The results of our study revealed regional separation
of populations between these two areas. Our analysis supports the
concept of regional groups of populations, and generally supports the
concordance in patterns of population differentiation derived from
analysis of allozymes and mitochondrial DNA variation. Allendorf and
Seeb (2000) reported a concordance between results from allozyme and
microsatellite analyses of population structure for sockeye salmon (O.
nerka).

Genetic differentiation of Russian chum salmon generally follows a
regional structure because proximate populations are generally more
similar to each other than to more distant populations. However, there
were some cases of populations from one region clustering with
populations from another region. One example was the Utka River
population from west Kamchatka clustering with populations from
northeast Russia. An association between the Utka River population and
the Anadyr River population was also reported by Winans et al. (1994) in
an analysis of allozyme variation. Because Winans et al. (1994) and
authors of the present study analyzed the same sample from the Utka
River population, concurrence between the allozyme and microsatellite
analyses was not unexpected. However, the number of fish sampled for the
Utka River population was the fewest for any of the west Kamchatka
populations (Table 1), and it may be that an increase in sample size for
this population would result in estimated allele frequencies that would
be more similar to those of other populations in west Kamchatka.
Additionally, the Tugur River population was most closely associated
with the population from the Amur River. In recent geologic history, the
Tugur River may have been once part of the Amur River drainage, but now
flows into Tugur Bay on the Sea of Okhotsk. A common origin between the
Amur River and Tugur River populations may account for the current
association between the two populations.

Distinctive groups of populations surveyed were those from the
Primorye, Sahkalin Island, the northern Sea of Okhotsk, and northeast
Russia, and a strong regional clustering of these populations was
observed in the dendrogram analysis. Most of these population groups
were characterized by slightly lower genetic variation compared with
other populations surveyed in Russia. For other salmonids, populations
from regions with reduced genetic variation have formed distinctive
clusters in dendrogram analysis (Beacham et al., 2006). Russian chum
salmon populations displayed on average less genetic differentiation
than did populations from western Alaska and adjacent areas. Despite the

Russian populations being surveyed from a larger geographic area
than were populations in western Alaska (Beacham et al., in press), and
thus there was greater likelihood of differentiation due to isolation by
distance, comparisons of locus-specific [F.sub.st] values between the
two groups indicated that Russian populations were less differentiated
(lower values in 11 of 14 loci, P=0.057). This result indicates that
there may be more straying among Russian populations than among those in
western Alaska, possibly as a result of adaptation to harsh enviromental
conditions or hatchery development and broodstock transfer in Russia.
Alternatively, less differentiation would also be observed if Russian
chum salmon had colonized available habitats more recently than had chum
salmon in western Alaska.

Stock identification

Accurate, economical, and practical methods of stock identification
are required to determine the migration pathways of juvenile and
maturing salmon, and to manage fisheries that may intercept salmon
during their migration to natal spawning grounds. Effective stock
identification techniques are based on characters that display stable
differentiation among groups to be discriminated, and these characters
must be examined easily in a rapid and cost-effective manner. Allozymes
provided the characters for initial genetically based population surveys
and stock identification of Russian chum salmon (Winans et al., 1994,
1998). Later, single nucleotide polymorphisms (SNPs) were used to
estimate the genetic structure of the population (Sato et al., 2001);
therefore estimation of stock composition in mixed-stock samples can
proceed (Sato et al., 2004). In an analysis of 30 haplotypes from mtDNA,
Sato et al. (2004) were able to indicate some level of regional
structure in populations in Russia, but the exact nature of the
geographic structure was uncertain. In our analysis of microsatellite
variation, clear differentiation was observed among regional groups of
populations, and populations from Primorye were the most distinctive.

Accuracy of estimated stock compositions is directly influenced by
the baseline used in the estimation procedure, and the level of genetic
differentiation among regional groups of populations is a key component.
However, sample sizes of populations in the baseline are also an
important part because the accuracy of estimation is related to the
population sample size (Beacham et al., 2006). Fewer than 60 fish were
sampled for many of the populations sampled in our survey, and
increasing sample sizes to approximately 150 fish per population would
likely lead to all regional estimates of stock composition being in
excess of 90% accurate in all simulated single-region mixture samples.

Surveys of genetic variation of salmon populations allow stock
identification in mixed-stock fisheries, where the origins of fish
contributing to mixed-stock fisheries are determined by comparing the
genetic characteristics of fish in the fishery samples to the genetic
characteristics of fish from potentially contributing populations.
Analysis of simulated mixed-stock samples of known origin is an initial
practical method to evaluate the potential for applying genetic
variation to mixed-stock fishery analysis. Our analysis of simulated
mixtures indicated that microsatellite variation provides accurate
estimates of regional contributions of chum salmon stocks from Russia,
and in some cases provides reliable estimates of individual populations
in simulated mixtures. Microsatellites have previously been reported to
provide reliable estimates of stock composition in mixed-stock chum
salmon samples of largely North American origin (Beacham et al., in
press), and our results from simulated mixtures indicated that
microsatellites should provide reliable estimates of stock composition
for chum salmon in coastal waters in Russia. However, if Japanese or
Korean chum salmon or potentially North American chum salmon are
intercepted in coastal or nearshore fisheries in Russia, then clearly a
larger baseline than the one examined in the current study would be
required to provide reliable estimates of stock composition under these
circumstances.

The application of microsatellites for the determination of
population structure of Russian chum salmon will allow significant
regional differentiation among these populations to be employed in
estimating regional contributions to mixed-stock fishery samples from
coastal waters. Microsatellites provide similar results for other
Pacific salmon species (Beacham et al., 2005, 2006) and are likely to be
effective in identifying the origin of Russian chum salmon in
mixed-stock fisheries in nearshore and offshore waters.

The present analysis of microsatellite variation of Russian chum
salmon provides evidence of a more fine-scale population structure than
those that have previously been demonstrated with other genetic-based
markers such as allozymes (Winans et al., 1994; Efremov, 2001) or
mitochondrial based SNPs (Sato et al., 2004). This more fine-scale
resolution of population structure was likely due to the larger number
of alleles associated with the microsatellite loci than with either the
allozyme or SNP loci. Because genetic-based markers generally exhibit
annual stability in allele frequencies, they are generally more
effective for stock identification applications than are techniques that
rely on environmentally induced variation to discriminate among stocks,
such as scale-pattern analysis of trace elements in otoliths. Once the
baseline has been established for genetic applications, annual surveys
of contributing stocks are not necessary, as is the case with for
environmentally induced variation. Should greater resolution in stock
composition estimates be required than that provided by the 14
microsatellites surveyed in the present study, the addition of markers
specifically designed to provide the required resolution will be
necessary. These markers could either be additional microsatellites, or
perhaps single nucleotide polymorphisms (SNPs) (Smith et al., 2005). It
is likely that a combination of microsatellites and SNPs can be employed
to provide accurate population or regional estimates of stock
composition of mixed-stock samples.

Acknowledgments

A significant effort was undertaken to collect samples from chum
salmon populations analyzed in the study. Samples of populations from
Primorye and Sakhalin Island, as well as some Magadan samples, were
initially provided by V. V. Efremov to the United States National Marine
Fisheries Service (NMFS) Auke Bay Laboratory, where R. Wilmot provided
access to the Molecular Genetics Laboratory (MGL). G. Winans of the
NMFS-Montlake laboratory also provided access to some population
samples. C. Wallace and J. Candy of the MGL assisted in the analysis.
Funding was provided by Fisheries and Oceans Canada.

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